A Cascaded Face Detection Framework

J. Zhu, S.C. Schwartz, and B. Liu (USA)

Keywords

face detection, computer vision, pattern recognition

Abstract

Face detection is an important basic technique for a
variety of image processing tasks. To improve its
applicability, we study fast face detection techniques
employing low complexity algorithms. This paper reports
on a cascaded face detection framework that incorporates
a feature-point detection component, and a probabilistic
modeling component based on a subregion division
scheme. Finally, a verification component that is
employed to improve the performance of the whole
system is discussed. Feature point detection takes
advantage of the ad hoc knowledge of the features in an
upright frontal face. By detecting those points frequently
appearing in the area of eyes and mouth, and then
heuristically and sequentially screening out many input
regions in an image based on the geometric features, this
step speeds up considerably the detection process. The
low complexity statistical modeling of the subregions in a
face, i.e., three horizontal strips and nine rectangular
blocks, further excludes some candidate face regions.
Experimental results demonstrate that this low complexity
cascaded system has good detection performance.